Chris W. Govaerts

Recent clinical trials have demonstrated the efficacy of different modalities of systemic therapy in treating brain metastases, including immune checkpoint inhibitors and various types of targeted therapy. These findings expand treatment options for these intracranial tumours, which are linked to significant morbidity and poor outcomes. Despite these advances, important challenges remain. These include the difficulty of identifying predictors of intracranial response to such therapies. The intracranial activity of anti-tumour agents is dependent on the complex interplay between the cellular components of the tumour microenvironment, consisting of both brain-resident cell types and peripheral immune cells. A deeper understanding of these interactions is essential for identifying mechanisms that contribute to inter- and intratumoural heterogeneity in treatment sensitivity. This review provides a comprehensive overview of the mechanisms by which the constituent cell types of the tumour microenvironment of brain metastases contribute to tumour progression and therapy resistance. Herein, a conceptual model of the relevant biological pathways is outlined along with references to how specific vulnerabilities could be utilised to develop more effective therapeutic strategies.

Background: Pseudoprogression is known to occur after immune-checkpoint inhibitor (ICI) therapy in brain metastasis and can complicate clinical decision-making. Still, its incidence, timing, and clinical presentation remain unclear. A retrospective cohort study in melanoma and non-small cell lung cancer brain metastasis patients was conducted to address this. Materials and Methods: Brain metastasis patients showing progression on MRI according to response assessment in neuro-oncology brain metastases criteria after starting ICI therapy were included, irrespective of prior irradiation. Lesions were classified as tumour progression (TP) or pseudoprogression based on three-month radiological follow-up or histopathology. TP was assigned if progression was again shown at three months. Pseudoprogression was assigned if lesions showed stability, partial, or complete response at three months. ‘Non-classified’ lesions were those with new or changed treatment during follow-up. Results: A cohort of 98 patients with 233 lesions was included over a 13-year period; 170 lesions were considered non-classified, and 41 and 22 lesions were classified as TP and pseudoprogression respectively. This resulted in a lesion- and patient-specific incidence for pseudoprogression of 9.4% and 17.3% respectively. Due to the large number of lesions that could not be classified, as is the case in clinical practice, the reported incidence in this study is likely an underestimation and can be seen as a ‘minimum’ incidence rate. Ten pseudoprogression (45.5%) and 13 (31.7%) TP lesions were previously irradiated. Pseudoprogression occurred at a median of 2.7 months after starting ICI therapy. The only clinical feature distinguishing patients with TP from pseudoprogression was that TP patients were more likely to need dexamethasone for neurological symptoms. Conclusions: Pseudoprogression has a lesion-specific incidence rate of at least 9.4% and occurs at a median of 2.7 months after starting ICI therapy. Severe neurological symptoms requiring dexamethasone may be a clinical feature typical for TP.

Objectives: Radiation-induced changes (RIC) secondary to focal radiotherapy can imitate tumour progression in brain metastases and make follow-up clinical decision making unreliable. 11C-methyl-L-methionine-PET (MET-PET) is widely used for the diagnosis of RIC in brain metastases, but minimal literature exists regarding the optimum PET measuring parameter to be used. We analysed the diagnostic performance of different MET-PET measuring parameters in distinguishing between RIC and tumour progression in a retrospective cohort of brain metastasis patients. Methods: 26 patients with 31 metastatic lesions were included on the basis of having undergone a PET scan due to radiological uncertainty of disease progression. The PET images were analysed and methionine uptake quantified using standardised-uptake-values (SUV) and tumour-to-normal tissue (T/N) ratios, generated as SUVmean, SUVmax, SUVpeak, T/Nmean, T/Nmax-mean and T/Npeak-mean. Metabolic-tumour-volume and total-lesion methionine metabolism were also computed. A definitive diagnosis of either RIC or tumour progression was established by clinicoradiological follow-up of least 4 months subsequent to the investigative PET scan. Results: All MET-PET parameters except metabolic-tumour-volume showed statistically significant differences between tumour progression and lesions with RIC. Receiver-operating-characteristic curve and area-under the-curve analysis demonstrated the highest value of 0.834 for SUVmax with a corresponding optimum threshold of 3.29. This associated with sensitivity, specificity, positive predictive and negative predictive values of 70.59%, 78.57%, 80.00% and 68.75% respectively. Conclusions: MET-PET is a useful modality for the diagnosis of RIC in brain metastases. SUVmax was the PET parameter with the greatest diagnostic performance. Advances in knowledge: More robust comparisons between SUVmax and SUVpeak could enhance follow-up treatment planning.